Introduction
Linear programming techniques assume that the data used is perfect. In particular, when activating these features, you are assuming that the objective function and constraint coefficients are correct, rather than the best estimates available. If this assumption does not hold, the solutions found may be sub-optimal.
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The graph on the right hand side of Figure 31 provides a graphical representation of the selected storage cost. You can also simultaneously display several functions based on your selection of storage cost functions from the list on the left hand side. The lower the cost (Y-axis) the greater the incentive to retain, or carry-over the associated storage volume (X-axis) to the next time-step. The same information is presented in the table, and can be exported to a .CSV file if required.
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Use the Storage Break Points tab (Figure 2) to design cost functions. Begin by using the Storage Cost Function popthe Storage Cost Function pop-up menu to select a cost function that you defined in the Cost Functions list in thethe Summary tab. Next, select the storage that should be associated with this cost function.
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By default, Source provides four rows in the Break points table. Each row is associated with a percentage of the full supply storage volume, which is visible, and a cost, which is not visible. In the Break pointsBreak points table shown in Figure 42:
- The bottom-most 10% of the capacity of the storage is associated with a base cost of -11000.00;
- The next 40% (50%-10%) of the capacity of the storage has a cost of the base cost plus one unit of increment. Here, the increment is 10, so the cost will be -10990;
- The next 30% (80%-50%) of the capacity of the storage has a cost of -10980 (base plus two increments); and
- The remaining capacity of the storage has a cost of the base plus three increments: -10970.
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n is an integer in the range 1..n representing the carry-over number.
point is the storage percentage (eg. “10” for 10%) when carry-over arc n takes effect.
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You can also import storage targets from a .CSV file. The format of the file is shown in Table 3. Note that the column ordering in the .CSV file does not match the display in Figure 64.
Table 3. Storage targets (data file format)
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| Row | Column (comma-separated) | ||
|---|---|---|---|
| 1 | 2 | 3 | |
| 1 | Demand | Priority | Computation Sequence |
| 2..n | sname | pri | seq |
Wherewhere:
sname is the name of the supply point
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- Select the active scenario in the Project Hierarchy (Project Explorer);
- Locate the Arc-Node Network entry in the Model Parameters list (also Project Explorer) and enable it for recording;
- Run the scenario; and
- In the Values column of the Recording Manager (Complex time series, summary), locate and open View Multiple Supply Path Setup. The window will be similar to Figure 6.
Figure 6 is a work in process progress so most of the labels are inaccurate. The graph provides a measure of computational complexity (iterations to solve) at each time-step. Note that the peaks are clipped to the value of the Maximum Iterations parameter in this dialog.
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